Workshops
Workshop 1
23 Oct.

13:00-14:30

Small Studio

An Introduction to Graph Neural Network (GNN) andIts Applications in Drug Response Prediction
Coordinated byDennis Wang
Imperial College London
Learning outcomes:
1. Gain basic knowledge of GNNs and their application in biomedical data analysis.
2. Understand the process of developing a GNN model for drug response prediction through a live
demonstration.
3. Engage in hands-on learning and experimentation with the provided Google Colab notebook postworkshop.
4. Access additional materials and resources to further explore the subject.

Teaching delivery:
1.

2.

3.
4.

Interactive PowerPoint Presentation:
1. Intro to graph data in biomedical research
1. Protein-protein interaction, disease association network, and small molecule representation.
2.
Overview of popular GNN algorithms
1. Graph convolutional network, message passing graph neural networks, and their variants.
3.
Case Studies
1. Successful applications of GNN in predicting drug responses.
Demo Demonstration:
1. Conduct a high-level walkthrough of a pre-prepared GNN model on drug response prediction.
2. Demonstrate the steps involved in building and understanding a GNN model using Pytorch andtorch_geometric, without delving into complex coding details.
3. Show the evaluation of the model.
Discussion + Q&A.
Supplementary Materials:
1. Access to the presentation slides and take-home Jupyter notebook on Colab for future reference.
1. Step-by-Step Tutorial: Building a basic GNN model for drug response prediction.
2. Dataset Overview: Understanding and preparing biomedical data for GNN.
3. Model Training and Evaluation: Practical insights into tuning and interpreting GNN models.
2.

List of key resources for further learning: Articles, tutorials, and datasets.

Workshop 223 Oct.

14:50-16:20

Small Studio

Exploring the ISCB Competency Framework Version 3.0
Coordinated byISCB
International Society for Computational Biology
Workshop Objective:
The workshop aims to familiarize participants with the ISCB Competency Framework, with focus on the latest
Version 3.0 release, covering some history of its development, understanding the competencies themselves, andexploring how one can apply the competency framework in various contexts in bioinformatics education andtraining.

Workshop Outline:**Introduction (10 minutes)**- Welcome and introduction to the workshop.
- Brief overview of the importance of competency frameworks in bioinformatics education and training.
- Overview of the ISCB Competency Framework development process.
**Understanding the ISCB Competency Framework 3.0 (25 minutes)**- Introduction to the ISCB Competency Framework Version 3.0.
- Overview of the competency components: knowledge, skills, attitudes.
- Explanation of how competencies apply to distinct training needs and personas.
- Guided exploration of the competency framework website:
https://competency.ebi.ac.uk/framework/iscb/3.0/competencies**Application of the Framework (45 minutes)**- Discussion and case studies on how the ISCB Competency Framework can be utilized in various contexts:- Evaluating an existing training program- Evaluating a short course- Developing a new training program or short course- Developing a program or course endorsement process**Wrap-Up and Q&A (10 minutes)**- Summary of key takeaways from the workshop.
- Open floor for questions and discussion.
- Closing remarks and gratitude.

Workshop 3
24 Oct.

10:45-12:15

Small Studio

Spatial Transcriptomics Data Analysis
Coordinated byShinn-Ying Ho, Tzong-Yi Lee,National Yang Ming Chiao Tung University, Taiwan

Workshop Description:

In the rapidly evolving field of high-throughput sequencing, spatial transcriptomics has emerged as a transformativetechnology, offering unprecedented insights into the spatial organization of gene expression within tissues andorganisms. This workshop aims to equip participants with the knowledge and tools necessary to harness the powerof spatial transcriptomics data and extract meaningful biological insights. The workshop will be held in interactive
manner, after the introduction of principles, methodologies, and challenges associated with spatial transcriptomics
data analysis. Through a combination of lectures, hands-on exercises, and interactive discussions, participants willlearn how to preprocess spatial transcriptomics datasets, perform quality control assessments, detect spatially coexpressed genes, and visualize spatial gene expression patterns.

Benefits of the Workshop to attendees:

From understanding the fundamentals of spatial gene expression profiling to exploring advanced computationaltechniques, participants will gain a comprehensive understanding of the analytical pipeline required to interpret
spatially resolved transcriptomic data effectively. Additionally, we will explore cutting-edge computational
approaches for spatial data integration, trajectory inference, and spatial cell type mapping, providing participants
with a toolkit to address diverse biological questions across a range of spatial scales and experimental designs.
Whether you are a biologist seeking to leverage spatial transcriptomics to uncover the spatial dynamics of gene
regulation or a computational scientist interested in developing novel analytical methods for spatial data analysis,
this workshop offers a valuable opportunity to enhance your skills and expand your research horizons. Join us as
we embark on a journey through the transcriptomic landscape, where spatially resolved insights await discovery.
In this workshop on spatial transcriptomics, we will focus on the effective processing, analysis, and visualizationof spatial transcriptomics data using Seurat and stLearn. Seurat, a comprehensive R package, has been
extended to support spatial data with functionalities beyond basic single-cell analysis, including preprocessing,quality control, normalization, and identification of spatially relevant clusters and markers. Meanwhile, stLearn,
designed to further enhance spatial data analysis, provides tools to integrate spatial information directly into theanalysis pipeline, enabling deeper insights into the spatial organization of cells. Both tools employ robust
statistical and machine learning methods such as PCA, t-SNE, and UMAP, which are critical for uncovering
complex data structures and understanding the spatial context of gene expression. The workshop will also
incorporate essential resources such as the Allen Brain Atlas, which provides comprehensive gene expression
datasets invaluable for researchers wishing to compare their spatial transcriptomics data with established brain
gene expression patterns. By integrating Seurat and stLearn with these databases, the workshop aims to provide
participants with a comprehensive framework for conducting spatial transcriptomics research.

Speakers:
Tzong-Yi Lee, National Yang Ming Chiao Tung University, Taiwan
Chia-Ru Chung, National Central University, Taiwan
Yuxuan Pang, The University of Tokyo

Workshop 4
24 Oct.

15:05-16:35

Small Studio

Glycoinformatics
Coordinated by:

Kiyoko F. Aoki-Kinoshita, Soka University, JapanRene Ranzinger, CCRC, University of Georgia, USA
Issaku Yamada, The Noguchi Institute, Japan
Workshop Description:
The Glycoinformatics workshop will be held in an interactive manner, after some shortpresentations by softwareand database developers. The content will cover basic glycandrawing tools/software, glycan format converters,
followed by more in-depth analyticaltools including GlycoMaple.

Benefits of the Workshop to attendees:This Workshop will benefit researchers who would like to either (a) find information or references to glycanrelated genes, proteins, lipids, lectins, pathways, diseases, etc. or (b) perform their own analysis on their datausing the latest tools developed by the speakers, including machine-learning tools and Web tools for analyzing
glycans and pathways, etc. After a set of invited talks by the developers of these databases and tools, thespeakers can be directly asked questions regarding their data or tool during panel discussion. This will also be an
opportunity for attendees to make requests for more data or tools that would be useful for their research;
opportunities for new collaborations are possible.

Structure: Invited talks (demos), Panel Discussion, or hands-on workshopSpeakers:
Kiyoko F. Aoki-Kinoshita, Soka University, Japan
Rene Ranzinger, CCRC, University of Georgia, USA
Issaku Yamada, Noguchi Institute, Japan
Philip Toukach (online)
Thomas Luetteke (online)